Mithun

Sivapathasundram

About

Acomputersciencegraduatewithapassionforbuildingclean,performantsoftware.

Interestedinfull-stackdevelopment,machine learning,andtheintersectionofgreatengineeringandgreatdesign.

Based in

Toronto, ON

Education

BSc Computer Science @

Toronto Metropolitan University

Graduated · 2026

Looking for

2026 New Grad Roles

Get in touch

Experience

Software Engineer Intern

May 2025 – Dec 2025

Environment and Climate Change Canada · Toronto, ON

Built features and hardened data integrity on the Environmental Emergency Regulations reporting system, where 12,000+ facilities across Canada report hazardous substance holdings.

Built 15+ features on a 3-developer team for the Environmental Emergency Regulations reporting system, a C#/.NET 8 platform where 12,000+ facilities across Canada report hazardous substance holdings: facility transfer workflows, bulk import tooling, automated notifications
Wrote the MS SQL stored procedures that detect and repair corrupted facility records, fixing 4,000+ records and retiring a weekly manual validation check
Added .NET validation rules and MS SQL constraints across the reporting intake path so malformed submissions are rejected at entry rather than downstream
Diagnosed and fixed production defects across the frontend and .NET services, raising Azure DevOps-reported test coverage from 65% to 85%

C#.NET 8Entity FrameworkMS SQLAzure DevOpsXML

Software Engineer Intern

Jan 2024 – Sep 2024

Ontario Treasury Board Secretariat · Toronto, ON

Shipped full-stack features and data automation for the legacy web applications that guide Ontario Ministry of Finance staff through tax appeal workflows.

Shipped full-stack features across two legacy web applications that guide Ontario Ministry of Finance staff through tax appeal workflows: required steps, deadlines, and the parties to notify at each stage
Added client-side validation and REST API integrations to those applications, standardizing how appeal data was entered and cutting downstream processing errors
Designed the relational schema and query layer behind an automated ETL pipeline that replaced a weekly manual appeals report with same-day delivery to Ministry of Finance analysts
Built a library of reusable, responsive front-end components in custom CSS from Figma specs, working with UI/UX designers from wireframe to production
Built and documented the REST APIs behind the appeals applications, gated behind automated tests in CI/CD before merge
Built a classifier on Power Automate's AI Builder that flagged AI-written job applications, placing 2nd of 30+ teams at an internal hackathon

JavaScriptHTML & CSSSQLPower BIREST APIsAzure DevOps

Projects

2026

Fantasy Basketball Companion

An LLM coaching app that recommends fantasy basketball roster moves, built solo end to end.

Next.jsTypeScriptNode.jsPostgreSQLRedisLangChainDockerKubernetesGKE

Fantasy Basketball Companion

GitHub Live

→Designed and built the full stack solo in Next.js and TypeScript: an LLM coaching app that recommends fantasy basketball roster moves
→Split the original Next.js/Supabase monolith into three independently deployable services on GKE Autopilot using Docker multi-stage builds, with a HorizontalPodAutoscaler scaling the AI inference service 1 to 5 replicas on CPU load so inference cost scales separately from the stateless frontend
→Backed the app with REST endpoints over JSON, a PostgreSQL data model, and Redis caching for repeated queries, with tests gated by GitHub Actions
→Wired LangChain.js with ConversationBufferMemory so the coaching engine keeps context across roster questions, streamed to the client

2025–2026

NBA Predictive Analytics Platform

End-to-end ML pipeline predicting per-game stat lines for 500+ active NBA players.

Pythonscikit-learnFlaskpytestGitHub Actions

NBA Predictive Analytics Platform

GitHub Live

→Built an end-to-end ML pipeline that predicts per-game stat lines for 500+ active NBA players, combining six ensemble models with automated daily retraining on fresh season data
→Built a leakage-free walk-forward eval harness over 3 backfilled seasons (79k player-games); trained it on real outcomes to beat the baseline by 2.9% MAE (4.59 vs 4.73, p < 0.001) across three validation protocols
→Built a Python ETL job on GitHub Actions that scrapes and normalizes Basketball Reference statistics daily into a clean, versioned dataset, no manual steps

2026

SimpleBL

Retrieval-augmented research assistant that grounds every answer in live PubMed literature.

TypeScriptReactNode.jsExpress.jsLangChainGroq APIPubMed / NCBI

SimpleBL

GitHub Live

→Built and deployed a retrieval-augmented research assistant that pulls matching PubMed articles per query and grounds every answer in those sources via LangChain and the Groq API
→Wrote a 340ms inter-request limiter to stay under NCBI's 3 req/s policy, with graceful degradation so PubMed outages surface as a clear message rather than an unhandled error
→Rendered each answer beside its source articles in a typed React client with centralized state, so users can trace any claim to a citation

2025

VetConnect

School Project: Full-stack veterinary management system connecting pet owners and clinics.

TypeScriptReactSupabasePostgreSQLHTML & CSS

VetConnect

→Collaborated with 4 developers to design and ship a secure, real-time patient management platform handling appointments, records, and messaging.
→Implemented role-based access control via Supabase Row-Level Security, ensuring strict data privacy between pet owners and veterinary staff.

2024

Smart Recruiter

AI-powered recruitment platform. Placed 2nd at the OPS Phenomenal Hackathon.

Microsoft Power AppsJavaScriptREST APIsMachine Learning

Smart Recruiter

→Reduced manual screening time by integrating Microsoft's Category Classification Model to flag AI-generated job applications.
→Delivered a full end-to-end platform within a tight hackathon timeline using Microsoft Power Apps and REST APIs.

2024

Olympic Medal Predictor

Linear regression model forecasting Olympic medal counts from 100+ years of historical data.

Pythonpandasscikit-learnseabornJupyter Notebook

Olympic Medal Predictor

GitHub

→Trained and evaluated the model across multiple Olympic Games, achieving strong predictive accuracy for country-level medal outcomes.
→Built end-to-end data cleaning, feature engineering, and interactive visualisations using pandas, seaborn, and scikit-learn.

2024

Soccer League Database System

School Project: Relational database system managing all data for a competitive soccer league.

SQLOracle DBJava

Soccer League Database System

→Designed a normalised Oracle DB schema covering teams, players, fixtures, results, and standings with full referential integrity.
→Conducted end-to-end testing to verify data consistency and reliability across concurrent read/write operations.

2023

Java Stock Market System

School Project: OOP stock market simulator with real-time data, built by a team of 6.

JavaJUnitAlpha Vantage API

Java Stock Market System

→Engineered comprehensive UML models to drive a clean, extensible OOP architecture across a 6-person team.
→Integrated the Alpha Vantage API for live stock data and achieved full unit test coverage with JUnit.

Skills

Languages

PythonJavaTypeScriptJavaScriptC#SQLBashHTMLCSS

Frameworks/Libraries

React.jsNext.js.NET 8Entity FrameworkFlaskTailwind CSSpandasscikit-learnRequestsSeleniumRedisLangChain

Databases & Cloud

PostgreSQLMS SQLMongoDBSupabaseDockerKubernetesGKEAzureHugging Face SpacesVercel

Tools & Workflows

GitGitHub ActionsFigmaCI/CDTest-Driven DevelopmentAgile MethodologiesREST APIsMLOpsPowerBIOutSystemsClaude CodeGitHub Copilot

Testing

VitestReact Testing LibraryPlaywrightpytest

Let's work together!

Full-stack developer | 2026 new grad | Passionate about building software that makes an impact.

Version

2026 © Edition

Timezone

EST (GMT‑5)

Socials

GitHubLinkedIn